Understanding chromatin structure: Efficient computational implementation of polymer physics models

In recent years the development of novel technologies, as Hi-C or GAM, allowed to investigate the spatial structure of chromatin in the cell nucleus with a constantly increasing level of accuracy. Polymer physics models have been developed and improved to better interpret the wealth of complex information coming from the experimental data, providing highly accurate understandings on chromatin architecture and on the mechanisms regulating genome folding. To investigate the capability of the models to explain the experiments and to test their agreement with the data, massive parallel simulations are needed and efficient algorithms are fundamental. In this work, we consider general computational Molecular Dynamics (MD) techniques commonly used to implement such models, with a special focus on the Strings & Binders Switch polymer model. By combining this model with machine learning computational approaches, it is possible to give an accurate description of real genomic loci. In addition, it is also possible to make predictions about the impact of structural variants of the genomic sequence, which are known to be linked to severe congenital diseases.